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Mistral · Open weights

Mistral Small 3.2 24B (2506)

Added Apr 15, 2025Open weights

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Use in the app

The latest iteration of Mistral Small, version 3.2 (2506) brings enhanced performance and capabilities. With 24 billion parameters, this model delivers state-of-the-art results across text generation tasks with improved efficiency.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.40

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K

Side by side

Mistral Small 3.2 24B (2506) compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Mistral Small 3.2 24B (2506)MistralThis modelQwen3.6 35B A3B TEEQwenRemove
ProviderMistralQwen
Discovery tierOpen weightsOpen weights
AddedApr 15, 2025Jun 9, 2026
Limits
Context window128K262.14KBest
Max output16.38K262.14KBest
Average output—3.9K tokens
Pricing, USD per 1M tokens
Input$0.20$0.20
Output$0.40Best$1.27
Performance
Request success (24h)100.0%Best99.5%
Benchmark indexes
Intelligence Index—15.2
Coding Index—28.1
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Mistral Small 3.2 24B (2506)
  • Qwen3.6 35B A3B TEE

Intelligence Index

  • Mistral Small 3.2 24B (2506)—
  • Qwen3.6 35B A3B TEE15.2
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 28.1

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 847 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 64.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 13.9%

  • GPQA Diamond

    Graduate-level science questions

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 81.7%

  • IFBench

    Following precise output instructions

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 36.2%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 25.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 85.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Mistral Small 3.2 24B (2506): —
    • Qwen3.6 35B A3B TEE: 64.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/mistral-small-3.2-24b-instruct",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="mistralai/mistral-small-3.2-24b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Mistral

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